Modern training methods are capable of discovering high-performance parameters for neural networks on a variety of tasks. Although models trained with similar procedures tend to exhibit similar in-domain (ID) performance on these tasks, they exhibit diverse decision boundaries [1]}. In particular, models with similar performance can make significantly different judgments when presented with examples that fall far from the training data manifold [2]}. In computer vision, these discussions of generalization often focus on visualizations of decision boundaries learnt by the different models.
